Parametric appraisal and wear prediction of hybrid composites using an integrated soft computing approach

Abstract This article reports on the implementation of artificial neural network (ANN) and statistical methods to analyze and predict the erosion performance of titania (TiO ₂ ) filled ramie‐epoxy based composites. These hybrid composites are prepared by conventional hand lay‐up route and subjected to solid particle erosion tests as per Taguchi's L ₂₇ orthogonal array. The effects of different control factors on erosion rate of these composites are studied using Taguchi method. It reveals that impact velocity and filler content have significant effect on the erosion wear rate followed by other least significant factors. The individual effect of each control factor while keeping other factors constant is ascertained by performing steady state erosion experiments. Further, a computational model based on ANN is used as a tool to effectively predict the erosion rates of the composites. The results show that the predicted values are in reasonably good agreement with the experimental ones with an accuracy of 90% and relative error lying within a range of 1%–10%. Further, the trained ANN model is validated by considering the erosion rates obtained during steady state erosion process as the input parameter. The possible mechanisms causing the wear loss are identified using electron microscopy. Highlights Successful fabrication of titanium oxide reinforced hybrid composites. Steady state erosion experiments are performed. Erosion rates are predicted using ANN and compared with the experimental data. Wear loss mechanisms are identified using electron microscopy.